SCH: Integrating AI and System Engineering for Glucose Regulation in Diabetes

该方案的目标是开发下一代多变量自动控制的原型 胰岛素释放系统(又称人工胰腺)通过系统工程和 人工智能(Al)技术，将减轻饮食、体力活动、急性 心理压力诱因和睡眠不规律，无需用户手动输入，严密 调节糖尿病患者的血糖水平。第一代自动化胰岛素注射 (AID)系统依靠混合闭环技术，从连续的血糖监测中收集数据 并且需要用户手动输入以减轻用餐和锻炼的影响。多变量 我们开发的AID提供了一个集成良好的下一代系统，可以分析历史和实时 来自不同来源的数据，包括连续血糖监测系统、胰岛素泵和 腕带体力活动跟踪器中的可穿戴传感器，可减轻进餐、体力活动、 和剧烈的心理压力，不需要用户手动输入。吃饭，有计划的锻炼，很多 日常生活的体力活动、急性心理压力和睡眠不规律会影响血糖。 水平不同，挑战1型糖尿病患者不断考虑所有这些复杂的 将他们的血糖水平维持在目标范围内的因素。血糖的进一步改善 监管可以通过开发新的、可解释的和交互式的人工智能技术来实现，这些技术可以 向医疗保健提供者和艾滋病患者解释他们的预测，并通过集成这些人工智能技术 运用系统工程技术开发Al-MvAID系统。这些铝技术的作用 是根据历史趋势和当前数据预测一个人的状态，并提供其他 对mvAID系统有价值的信息，将用户从繁重的重复口译任务中解脱出来 他们目前的代谢状态，预测他们目前的行为对未来血糖变化的影响 并调整Al-mvAID控制器的参数。目标是产生一个强大的、用户友好的 将新的AI技术与mvAID系统集成在一起的技术，可将用户负担降至最低 实现对血糖水平的严格控制，尽管在自由生活中发生了许多复杂的血糖紊乱 条件，如用餐、体育活动、急性心理压力和睡眠不规律。

The objective of this proposal is to develop a prototype for the next generation multivariable automated insulin delivery (mvAID) systems (also called artificial pancreas) by integrating systems engineering and artificial intelligence (Al) techniques that will mitigate the effects of meals, physical activities ,acute psychological stress inducements and sleep irregularities without manual inputs by the user to tightly regulate the glucose levels of people with diabetes. The first generation of automated insulin delivery (AID) systems relied on hybrid closed-loop technology, collecting data from continuous glucose monitoring devices and requiring manual user inputs for mitigating the effects of meals and exercise. The multivariable AID that we developed provides a well-integrated next-generation system that analyzes historical and realtime data from different sources, including continuous glucose monitoring systems, insulin pumps, and wearable sensors in wristband physical activity trackers, to mitigate the effects of meals, physical activities, and acute psychological stress without manual inputs by the user. Meals, planned exercises, many physical activities of daily living, acute psychological stress, and sleep irregularities affect blood glucose levels differentially, challenging people with Type 1 diabetes to continuously consider all these complex factors in maintaining their blood glucose levels in the target range. Further improvement in glucose regulation can be achieved by developing novel, interpretable, and interactive Al techniques that can explain their predictions to medical care providers and AID users, and by integrating these Al techniques with systems engineering techniques to develop an Al-mvAID system. The function of these Al techniques is to predict the state of a person based on historical trends and current data, and provide additional valuable information to the mvAID system to relieve the users from onerous repetitive tasks for interpreting their current metabolic state, predicting the impact of their current actions on future variations in glucose levels, and tuning the parameters of the Al-mvAID controller. The goal is to produce a powerful userfriendly technology that integrates novel Al techniques with mvAID systems for minimal user burden in achieving tight control of glucose levels despite the many complex glycemic disturbances occurring in freeliving conditions, such as meals, physical activities, acute psychological stress, and sleep irregularities.